Increasing evidence shows that microbes are closely related to various human diseases. Obtaining a comprehensive and detailed understanding of the relationships between microbes and diseases would not only be beneficial to disease prevention, diagnosis and prognosis, but also would lead to the discovery of new drugs. However, because of a lack of data, little effort has been made to predict novel microbe-disease associations. To date, few methods have been proposed to solve the problem. In this study, we developed a new computational model based on network consistency projection to infer novel human microbe-disease associations (NCPHMDA) by integrating Gaussian interaction profile kernel similarity of microbes and diseases, and symptom-based disease similarity. NCPHMDA is a non-parametric and global network based model that combines microbe space projection and disease space projection to achieve the final prediction. Experimental results demonstrated that the integrated space projection of microbes and diseases, and symptom-based disease similarity played roles in the model performance. Cross validation frameworks and case studies further illustrated the superior predictive performance over other methods.
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